Elisa Negri

Requirements and languages for the semantic representation of manufacturing systems

HighlightsThe requirements that the languages for the semantic and ontological modelling of the manufacturing domain should present are collected.A review of the manufacturing domain semantic modelling is performed.A possible role of ontologies in the manufacturing domain is envisioned, that is to be used as knowledge bases that support Service oriented Control Architectures based on Web Services within the open automation paradigm.An industrial case is presented that shows the possible use in a real context of the ontology for manufacturing domain. In the last years, attention has been devoted to the development of ontologies, which are ICT conceptual models allowing a formal and shared representation of a particular domain of discourse, and to the use of these representations in a variety of contexts, among which also the industrial engineering can be counted. Within the industrial engineering field, the manufacturing domain has not yet seen a wide application of ontologies. This paper firstly shows the use of ontologies for the semantic annotation of a Web Service-based architecture for the control of manufacturing systems; and then contributes to the research field of manufacturing domain ontologies by proposing a thorough literature review and analysis of the available languages supporting such objective. The paper collects the main requirements that semantic languages must meet to be used in the manufacturing domain with the outlined purpose. In fact, the available semantic languages are several and characterized by different features: the paper identifies the most proper ones for the manufacturing domain representation thanks to their analysis against the main requirements. Lastly, the paper shows how the discussed topics are deployed in a real industrial example.

Ontology-Based Modeling of Manufacturing and Logistics Systems for a New MES Architecture

The paper illustrates the role of modeling of shop floor to support an innovative solution for the control architecture of automated manufacturing systems. One of the main characteristics of manufacturing systems domain is, in fact, the variety of configurations that manufacturing systems can assume and this may prevent the possibility to easily adapt and reconfigure the control solution for advanced manufacturing systems. To this end, the paper presents a proposal on how to cope with this issue, coming from a collaborative project, where important European universities and companies are involved. The proposal is based on a structured modeling (i.e. ontology) of manufacturing systems. The paper proposes a practical example of the modeling, envisioning how this can be then exploited within the proposed architecture that defines a new concept of the Manufacturing Execution System of manufacturing equipment.

A Maturity Model for Assessing the Digital Readiness of Manufacturing Companies

“The most profound technologies are those that disappear… They weave themselves into the fabric of everyday life until they are indistinguishable from it” wrote computer scientist and visionary Mark Weiser nearly 25 years ago in his essay “The Computer for the 21st Century.” It turns out he was right: in the age of “Industry 4.0”, digital technologies are the core driver for the manufacturing transformation. In fact, the introduction of such technologies allows companies to find solutions capable to turn increasing complexity into opportunities for ensuring sustainable competitiveness and profitable growth. Nonetheless, the effective implementation in manufacturing still depends on the state of practice: it may slow down, or even worst, may prevent from implementation. Indeed, we assume that a minimum level of capabilities is required before implementing the digital technologies in a company. Based on this concern, our research question is “are manufacturing companies ready to go digital?”. This paper wants to illustrate a “tool” to answer this question by building a maturity assessment method to measure the digital readiness of manufacturing firms. Based on the inspiring principles of the CMMI (Capability Maturity Model Integration) framework, we propose a model to set the ground for the investigation of company digital maturity. Different dimensions are used to assess 5 areas in which manufacturing key processes can be grouped: (1) design and engineering, (2) production management, (3) quality management, (4) maintenance management and (5) logistics management. Thus, the maturity model provides a normative description of practices in each area and dimension, building a ranked order of practices (i.e. from low to high maturity). A scoring method for maturity assessment is subsequently defined, in order to identify the criticalities in implementing the digital transformation and to subsequently drive the improvement of the whole system. The method should be useful both to manufacturing companies and researchers interested in understanding the digital readiness level in the state of practice.

Ontology for Service-Based Control of Production Systems

The paper illustrates a production systems ontology that models the discrete manufacturing, process production and the logistics domains. This ontology is used to allow semantic interoperability within a control architecture based on semantically-enriched Web Services that has been developed within the European funded project eScop. This architecture would facilitate the responsiveness and agility of the manufacturing companies, helping them to be more competitive thanks to the higher flexibility and re-configurability of their production systems.

Modelling internal logistics systems through ontologies

Proposal of a standard taxonomy and classification of internal logistics resources (warehousing in particular) that is expressed into object-oriented modelling.The object-oriented modelling is ready to be included into OWL ontology models, in particular it is proposed as extension to support the logistics field representation of the Manufacturing Systems Ontology (MSO).The paper suggests the role of ontologies and semantic representation as supporting elements of the modularity and flexibility of internal logistics systems.The concepts of Storage and Transporters are analysed, classified and hierarchically represented also in relation to other relevant concepts.The paper reports also an industrial example to show how the proposed modelling can be declined in reality. Industry is facing an era characterised by unpredictable market changes and by a turbulent competitive environment. The key to compete in such a context is to achieve high degrees of responsiveness by means of high flexibility and rapid reconfiguration capabilities. The deployment of modular solutions seems to be part of the answer to face these challenges. Semantic modelling and ontologies may represent the needed knowledge representation to support flexibility and modularity of production systems, when designing a new system or when reconfiguring an existing one. Although numerous ontologies for production systems have been developed in the past years, they mainly focus on discrete manufacturing, while logistics aspects, such as those related to internal logistics and warehousing, have not received the same attention. The paper aims at offering a representation of logistics aspects, reflecting what has become a de-facto standard terminology in industry and among researchers in the field. Such representation is to be used as an extension to the already-existing production systems ontologies that are more focused on manufacturing processes. The paper presents the structure of the hierarchical relations within the examined internal logistics elements, namely Storage and Transporters, structuring them in a series of classes and sub-classes, suggesting also the relationships and the attributes to be considered to complete the modelling. Finally, the paper proposes an industrial example with a miniload system to show how such a modelling of internal logistics elements could be instanced in the real world.

Generic platform for manufacturing execution system functions in knowledge-driven manufacturing systems

ABSTRACT Information technologies grow rapidly nowadays with the advance and extension of computing capabilities. This growth affects several fields, which consume these technologies. Industrial Automation is not an exception. This publication describes a general and flexible architecture for implementing Manufacturing Execution System (MES) function, which can be deployed in multiple industrial cases. These features are achieved by combining the flexibility of knowledge-driven systems with the vendor-independent property of RESTful web services. With deployment of this solution, MES functions may gain more versatility and independency. This research work is a continuation of the development of the OKD-MES (Open Knowledge-Driven Manufacturing Execution System) framework during the execution of the eScop project. The OKD-MES framework consists on a semantic-based solution for controlling and enhancing the flexibility and re-configurability of MES. In such scope, this research presents MES functions architecture that might be implemented in the OKD-MES framework in order to increase the flexibility of event-driven manufacturing systems.

Lean thinking in the digital Era

The Industry 4.0 concept represents a paradigm shift where physical objects are seamlessly integrated into information networks. This promises to enable a more effective infrastructure in which the design, development, manufacturing and support activities that represent the key parts of a product’s life cycle are closely integrated through the presence of real-time information and big data, arising from sensors, Cyber Physical Systems, Internet of Things and social networks. The challenge is to understand how to use this extensive information in order to enhance product value and to improve industrial productivity. Since information must be displayable, reusable and available in real-time, the fourth industrial revolution is already well-aligned with lean thinking, which promotes information visualization, including the just-in-time delivery of materials and information, as well as the zero defects ideal to quality management. Moreover, Lean thinking forces the development of human resource capabilities, through the adoption of scientific problem solving and continuous improvement approaches. These approaches must continue to underpin the leadership and employee development activities required in light of Industry 4.0. Through a systematic literature review, this paper describes the current state of the art in order to understand how lean thinking should be implemented in the context of the smart factory, and provides an initial contribution to the emerging debate around the roles of “Lean Thinking in the Digital Era”.

Intelligent decision-making models for production and retail operations

The book aims at providing an overview of various models and approaches to support decision-making in production and retail operations. Manufacturing and retail companies are facing new challenges ...

Continuous improvement planning through sustainability assessment of product-service systems

The paper presents a methodology for the integrated sustainability assessment of a product-service system lifecycle, with the purpose to support continuous improvement on the side both of the manufacturer and of the user. Its eight steps are an extension of ISO 14040 life cycle assessment and consider all three sustainability dimensions - economic, environmental and social - and a service perspective, using the service unit. A set of indicators for the three dimensions, aligned to the service unit concept, is proposed based on literature suggestions.

Economic and environmental impact assessment through system dynamics of technology-enhanced maintenance services

This work presents an economic and environmental impact assessment of maintenance services in order to evaluate how they contribute to sustainable value creation through field service delivery supported by advanced technologies. To this end, systems dynamics is used to assist the prediction of economic and environmental impacts of maintenance services supported by the use of an e-maintenance platform implementing prognosis and health management. A special concern is given to the energy use and related carbon footprint as environmental impacts.